{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br><br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Listed Volatility and Variance Derivatives\n",
    "\n",
    "**Dr. Yves J. Hilpisch &mdash; Wiley Finance (2016)**\n",
    "\n",
    "<img src=\"http://hilpisch.com/images/lvvd_cover.png\" alt=\"Derivatives Analytics with Python\" width=\"30%\" align=\"left\" border=\"0\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Variance Futures at Eurex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Variance Futures Concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Realized Variance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Net Present Value Concepts"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Discount Factor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import warnings; warnings.simplefilter('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 135 entries, 2015-01-02 to 2015-07-14\n",
      "Data columns (total 8 columns):\n",
      "1w     135 non-null float64\n",
      "2w     135 non-null float64\n",
      "1m     135 non-null float64\n",
      "2m     135 non-null float64\n",
      "3m     135 non-null float64\n",
      "6m     135 non-null float64\n",
      "9m     135 non-null float64\n",
      "12m    135 non-null float64\n",
      "dtypes: float64(8)\n",
      "memory usage: 9.5 KB\n"
     ]
    }
   ],
   "source": [
    "## read data from CSV file\n",
    "eb = pd.read_csv('data/hist_EURIBOR_2015.csv',  # filename\n",
    "                   index_col=0,  # index column\n",
    "                   parse_dates=True,  # parsing date information\n",
    "                   dayfirst=True)  # European date convention\n",
    "eb.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import seaborn as sns; sns.set()\n",
    "import matplotlib\n",
    "matplotlib.rcParams['font.family'] = 'serif'\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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4zM4pkcg3NCzgX//1/5HNZjGbzTQ2HiQQCHLjjTexfftbPPjgZ9i8+WWefPIn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pgUsmDLGGOMMSaFLNgyxhhjjEkhC7aMMcYYY1LIgi1jjDHGmBSyYMsYY4wxJoUs2DLG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119ac7fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eb.plot(figsize=(10, 6));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1w</th>\n",
       "      <th>2w</th>\n",
       "      <th>1m</th>\n",
       "      <th>2m</th>\n",
       "      <th>3m</th>\n",
       "      <th>6m</th>\n",
       "      <th>9m</th>\n",
       "      <th>12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>-0.131</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>-0.131</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>-0.133</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>-0.018</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>-0.131</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.038</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.102</td>\n",
       "      <td>0.166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>-0.131</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>-0.071</td>\n",
       "      <td>-0.039</td>\n",
       "      <td>-0.019</td>\n",
       "      <td>0.049</td>\n",
       "      <td>0.101</td>\n",
       "      <td>0.168</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               1w     2w     1m     2m     3m     6m     9m    12m\n",
       "2015-07-08 -0.131 -0.118 -0.071 -0.038 -0.018  0.049  0.101  0.164\n",
       "2015-07-09 -0.131 -0.118 -0.071 -0.039 -0.018  0.049  0.100  0.163\n",
       "2015-07-10 -0.133 -0.118 -0.071 -0.039 -0.018  0.049  0.101  0.164\n",
       "2015-07-13 -0.131 -0.118 -0.071 -0.038 -0.019  0.049  0.102  0.166\n",
       "2015-07-14 -0.131 -0.118 -0.071 -0.039 -0.019  0.049  0.101  0.168"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eb.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9983214104100598"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "math.exp(-0.00168 * 365. / 365)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### ARMVM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "DatetimeIndex: 60 entries, 2015-04-21 to 2015-07-14\n",
      "Data columns (total 1 columns):\n",
      "EONIA    60 non-null float64\n",
      "dtypes: float64(1)\n",
      "memory usage: 960.0 bytes\n"
     ]
    }
   ],
   "source": [
    "## read data from Excel spreadsheet file\n",
    "eo = pd.read_csv('data/hist_EONIA_2015.csv',  # filename\n",
    "                   index_col=0,  # index column\n",
    "                   parse_dates=True,  # parsing date information\n",
    "                   dayfirst=True)  # European date convention\n",
    "# eo.index = pd.DatetimeIndex(eo.index) \n",
    "eo.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11c6cfd90>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ZNfDuAl1cYqOi1kF9ay+F2SmRHp4QcaN/6PxitwDmBAM6ZEYtFkigJkLOZDTw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\nTURERKREKVETERERKVFK1ERERERK1P8HEO63Sl3fpVIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11c066c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "eo.plot(figsize=(10, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>EONIA</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-07-08</th>\n",
       "      <td>-0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-09</th>\n",
       "      <td>-0.121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-10</th>\n",
       "      <td>-0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-13</th>\n",
       "      <td>-0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-07-14</th>\n",
       "      <td>-0.112</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            EONIA\n",
       "2015-07-08 -0.118\n",
       "2015-07-09 -0.121\n",
       "2015-07-10 -0.120\n",
       "2015-07-13 -0.118\n",
       "2015-07-14 -0.112"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eo.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "r_eo = -0.001"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0002739722274380796"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARMVM3 = 0 * math.exp(r_eo / 365) + (3100 - 3000) * (math.exp(r_eo / 365) - 1)\n",
    "ARMVM3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.0004109575905493053"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARMVM4 = (ARMVM3 * math.exp(r_eo / 365)\n",
    "        + (3050 - 3000) * (math.exp(r_eo / 365) - 1))\n",
    "ARMVM4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Traded Variance Strike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Traded Futures Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Number of Futures"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Par Variance Strike"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "### Futures Settlement Price"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Example Calculation for a Variance Future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>3468.31</td>\n",
       "      <td>27.2996</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>3456.79</td>\n",
       "      <td>26.8020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>3526.48</td>\n",
       "      <td>25.8610</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>3551.91</td>\n",
       "      <td>26.3897</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>3502.77</td>\n",
       "      <td>29.7725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>3438.07</td>\n",
       "      <td>34.5593</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>3454.09</td>\n",
       "      <td>36.2222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>3428.76</td>\n",
       "      <td>34.7235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>3450.45</td>\n",
       "      <td>34.7235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>3455.80</td>\n",
       "      <td>34.7235</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5E     V6I1\n",
       "Date                        \n",
       "2015-06-01  3575.04  25.8710\n",
       "2015-06-02  3561.89  25.9232\n",
       "2015-06-03  3583.82  25.7958\n",
       "2015-06-04  3556.38  26.2418\n",
       "2015-06-05  3510.01  27.4496\n",
       "2015-06-08  3468.31  27.2996\n",
       "2015-06-09  3456.79  26.8020\n",
       "2015-06-10  3526.48  25.8610\n",
       "2015-06-11  3551.91  26.3897\n",
       "2015-06-12  3502.77  29.7725\n",
       "2015-06-15  3438.07  34.5593\n",
       "2015-06-16  3454.09  36.2222\n",
       "2015-06-17  3428.76  34.7235\n",
       "2015-06-18  3450.45  34.7235\n",
       "2015-06-19  3455.80  34.7235"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "h5 = pd.HDFStore('data/SX5E_V6I1.h5', 'r')\n",
    "data = h5['SX5E_V6I1']\n",
    "h5.close()\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>2w</th>\n",
       "      <th>EONIA</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>3468.31</td>\n",
       "      <td>27.2996</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>3456.79</td>\n",
       "      <td>26.8020</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>3526.48</td>\n",
       "      <td>25.8610</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>3551.91</td>\n",
       "      <td>26.3897</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>3502.77</td>\n",
       "      <td>29.7725</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>3438.07</td>\n",
       "      <td>34.5593</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.119</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>3454.09</td>\n",
       "      <td>36.2222</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>3428.76</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.110</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>3450.45</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>3455.80</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.120</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5E     V6I1     2w  EONIA\n",
       "Date                                      \n",
       "2015-06-01  3575.04  25.8710 -0.108 -0.106\n",
       "2015-06-02  3561.89  25.9232 -0.109 -0.122\n",
       "2015-06-03  3583.82  25.7958 -0.109 -0.143\n",
       "2015-06-04  3556.38  26.2418 -0.109 -0.138\n",
       "2015-06-05  3510.01  27.4496 -0.109 -0.115\n",
       "2015-06-08  3468.31  27.2996 -0.110 -0.127\n",
       "2015-06-09  3456.79  26.8020 -0.110 -0.126\n",
       "2015-06-10  3526.48  25.8610 -0.111 -0.117\n",
       "2015-06-11  3551.91  26.3897 -0.111 -0.120\n",
       "2015-06-12  3502.77  29.7725 -0.113 -0.125\n",
       "2015-06-15  3438.07  34.5593 -0.108 -0.119\n",
       "2015-06-16  3454.09  36.2222 -0.109 -0.125\n",
       "2015-06-17  3428.76  34.7235 -0.109 -0.110\n",
       "2015-06-18  3450.45  34.7235 -0.109 -0.118\n",
       "2015-06-19  3455.80  34.7235 -0.108 -0.120"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = data.join(eb['2w'], how='left')\n",
    "data = data.join(eo, how='left')\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>2w</th>\n",
       "      <th>EONIA</th>\n",
       "      <th>R_t</th>\n",
       "      <th>sigma**2</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
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       "      <td>-0.122</td>\n",
       "      <td>-0.003685</td>\n",
       "      <td>34.220799</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.143</td>\n",
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       "      <td>64.580457</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.007686</td>\n",
       "      <td>92.677552</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.013124</td>\n",
       "      <td>178.023718</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>3468.31</td>\n",
       "      <td>27.2996</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.127</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>214.408816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>3456.79</td>\n",
       "      <td>26.8020</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.003327</td>\n",
       "      <td>183.323049</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>3526.48</td>\n",
       "      <td>25.8610</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.019960</td>\n",
       "      <td>300.555651</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>3551.91</td>\n",
       "      <td>26.3897</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.007185</td>\n",
       "      <td>279.249096</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>3502.77</td>\n",
       "      <td>29.7725</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.013931</td>\n",
       "      <td>302.564935</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>3438.07</td>\n",
       "      <td>34.5593</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>-0.018644</td>\n",
       "      <td>359.901600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>3454.09</td>\n",
       "      <td>36.2222</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>332.134168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>3428.76</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.007360</td>\n",
       "      <td>315.833038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>3450.45</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>299.246550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>3455.80</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>278.303868</td>\n",
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       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "               SX5E     V6I1     2w  EONIA       R_t    sigma**2\n",
       "Date                                                            \n",
       "2015-06-01  3575.04  25.8710 -0.108 -0.106       NaN         NaN\n",
       "2015-06-02  3561.89  25.9232 -0.109 -0.122 -0.003685   34.220799\n",
       "2015-06-03  3583.82  25.7958 -0.109 -0.143  0.006138   64.580457\n",
       "2015-06-04  3556.38  26.2418 -0.109 -0.138 -0.007686   92.677552\n",
       "2015-06-05  3510.01  27.4496 -0.109 -0.115 -0.013124  178.023718\n",
       "2015-06-08  3468.31  27.2996 -0.110 -0.127 -0.011951  214.408816\n",
       "2015-06-09  3456.79  26.8020 -0.110 -0.126 -0.003327  183.323049\n",
       "2015-06-10  3526.48  25.8610 -0.111 -0.117  0.019960  300.555651\n",
       "2015-06-11  3551.91  26.3897 -0.111 -0.120  0.007185  279.249096\n",
       "2015-06-12  3502.77  29.7725 -0.113 -0.125 -0.013931  302.564935\n",
       "2015-06-15  3438.07  34.5593 -0.108 -0.119 -0.018644  359.901600\n",
       "2015-06-16  3454.09  36.2222 -0.109 -0.125  0.004649  332.134168\n",
       "2015-06-17  3428.76  34.7235 -0.109 -0.110 -0.007360  315.833038\n",
       "2015-06-18  3450.45  34.7235 -0.109 -0.118  0.006306  299.246550\n",
       "2015-06-19  3455.80  34.7235 -0.108 -0.120  0.001549  278.303868"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "data['R_t'] = np.log(data['SX5E'] / data['SX5E'].shift(1))\n",
    "data['sigma**2'] = 10000 * 252 * (np.cumsum(data['R_t'] ** 2)\n",
    "                                  / np.arange(len(data)))\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Date\n",
       "2015-06-01     1\n",
       "2015-06-02     2\n",
       "2015-06-03     3\n",
       "2015-06-04     4\n",
       "2015-06-05     5\n",
       "2015-06-08     6\n",
       "2015-06-09     7\n",
       "2015-06-10     8\n",
       "2015-06-11     9\n",
       "2015-06-12    10\n",
       "2015-06-15    11\n",
       "2015-06-16    12\n",
       "2015-06-17    13\n",
       "2015-06-18    14\n",
       "2015-06-19    15\n",
       "Name: t, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "T = 15.\n",
    "data['t'] = np.arange(1, 16)\n",
    "data['t']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
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       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>2w</th>\n",
       "      <th>EONIA</th>\n",
       "      <th>R_t</th>\n",
       "      <th>sigma**2</th>\n",
       "      <th>t</th>\n",
       "      <th>DF_t</th>\n",
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       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th></th>\n",
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       "  <tbody>\n",
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       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
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       "      <td>-0.106</td>\n",
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       "      <td>1</td>\n",
       "      <td>1.000038</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.003685</td>\n",
       "      <td>34.220799</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000036</td>\n",
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       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>64.580457</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.007686</td>\n",
       "      <td>92.677552</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.013124</td>\n",
       "      <td>178.023718</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>3468.31</td>\n",
       "      <td>27.2996</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.127</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>214.408816</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000025</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>3456.79</td>\n",
       "      <td>26.8020</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.003327</td>\n",
       "      <td>183.323049</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000022</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>3526.48</td>\n",
       "      <td>25.8610</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.019960</td>\n",
       "      <td>300.555651</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>3551.91</td>\n",
       "      <td>26.3897</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.007185</td>\n",
       "      <td>279.249096</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000016</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>3502.77</td>\n",
       "      <td>29.7725</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.013931</td>\n",
       "      <td>302.564935</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000014</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>3438.07</td>\n",
       "      <td>34.5593</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>-0.018644</td>\n",
       "      <td>359.901600</td>\n",
       "      <td>11</td>\n",
       "      <td>1.000011</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>3454.09</td>\n",
       "      <td>36.2222</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>332.134168</td>\n",
       "      <td>12</td>\n",
       "      <td>1.000008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>3428.76</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.007360</td>\n",
       "      <td>315.833038</td>\n",
       "      <td>13</td>\n",
       "      <td>1.000005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>3450.45</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>299.246550</td>\n",
       "      <td>14</td>\n",
       "      <td>1.000003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>3455.80</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>278.303868</td>\n",
       "      <td>15</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5E     V6I1     2w  EONIA       R_t    sigma**2   t      DF_t\n",
       "Date                                                                          \n",
       "2015-06-01  3575.04  25.8710 -0.108 -0.106       NaN         NaN   1  1.000038\n",
       "2015-06-02  3561.89  25.9232 -0.109 -0.122 -0.003685   34.220799   2  1.000036\n",
       "2015-06-03  3583.82  25.7958 -0.109 -0.143  0.006138   64.580457   3  1.000033\n",
       "2015-06-04  3556.38  26.2418 -0.109 -0.138 -0.007686   92.677552   4  1.000030\n",
       "2015-06-05  3510.01  27.4496 -0.109 -0.115 -0.013124  178.023718   5  1.000027\n",
       "2015-06-08  3468.31  27.2996 -0.110 -0.127 -0.011951  214.408816   6  1.000025\n",
       "2015-06-09  3456.79  26.8020 -0.110 -0.126 -0.003327  183.323049   7  1.000022\n",
       "2015-06-10  3526.48  25.8610 -0.111 -0.117  0.019960  300.555651   8  1.000019\n",
       "2015-06-11  3551.91  26.3897 -0.111 -0.120  0.007185  279.249096   9  1.000016\n",
       "2015-06-12  3502.77  29.7725 -0.113 -0.125 -0.013931  302.564935  10  1.000014\n",
       "2015-06-15  3438.07  34.5593 -0.108 -0.119 -0.018644  359.901600  11  1.000011\n",
       "2015-06-16  3454.09  36.2222 -0.109 -0.125  0.004649  332.134168  12  1.000008\n",
       "2015-06-17  3428.76  34.7235 -0.109 -0.110 -0.007360  315.833038  13  1.000005\n",
       "2015-06-18  3450.45  34.7235 -0.109 -0.118  0.006306  299.246550  14  1.000003\n",
       "2015-06-19  3455.80  34.7235 -0.108 -0.120  0.001549  278.303868  15  1.000000"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r_eb = -0.001\n",
    "data['DF_t'] = np.exp(-r_eb * (T - data['t']) / 365.) \n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "25.870999999999999"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sigma_K = data['V6I1'][0]\n",
    "sigma_K"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1932.665919369178"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Notional = 100000 / (2 * sigma_K)\n",
    "Notional"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>2w</th>\n",
       "      <th>EONIA</th>\n",
       "      <th>R_t</th>\n",
       "      <th>sigma**2</th>\n",
       "      <th>t</th>\n",
       "      <th>DF_t</th>\n",
       "      <th>F_tS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000038</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.003685</td>\n",
       "      <td>34.220799</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000036</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>64.580457</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000033</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.007686</td>\n",
       "      <td>92.677552</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000030</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.013124</td>\n",
       "      <td>178.023718</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000027</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>3468.31</td>\n",
       "      <td>27.2996</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.127</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>214.408816</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000025</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>3456.79</td>\n",
       "      <td>26.8020</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.003327</td>\n",
       "      <td>183.323049</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000022</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>3526.48</td>\n",
       "      <td>25.8610</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.019960</td>\n",
       "      <td>300.555651</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000019</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>3551.91</td>\n",
       "      <td>26.3897</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.007185</td>\n",
       "      <td>279.249096</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000016</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>3502.77</td>\n",
       "      <td>29.7725</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.013931</td>\n",
       "      <td>302.564935</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000014</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>3438.07</td>\n",
       "      <td>34.5593</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>-0.018644</td>\n",
       "      <td>359.901600</td>\n",
       "      <td>11</td>\n",
       "      <td>1.000011</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>3454.09</td>\n",
       "      <td>36.2222</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>332.134168</td>\n",
       "      <td>12</td>\n",
       "      <td>1.000008</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>3428.76</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.007360</td>\n",
       "      <td>315.833038</td>\n",
       "      <td>13</td>\n",
       "      <td>1.000005</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>3450.45</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>299.246550</td>\n",
       "      <td>14</td>\n",
       "      <td>1.000003</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>3455.80</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>278.303868</td>\n",
       "      <td>15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5E     V6I1     2w  EONIA       R_t    sigma**2   t  \\\n",
       "Date                                                                   \n",
       "2015-06-01  3575.04  25.8710 -0.108 -0.106       NaN         NaN   1   \n",
       "2015-06-02  3561.89  25.9232 -0.109 -0.122 -0.003685   34.220799   2   \n",
       "2015-06-03  3583.82  25.7958 -0.109 -0.143  0.006138   64.580457   3   \n",
       "2015-06-04  3556.38  26.2418 -0.109 -0.138 -0.007686   92.677552   4   \n",
       "2015-06-05  3510.01  27.4496 -0.109 -0.115 -0.013124  178.023718   5   \n",
       "2015-06-08  3468.31  27.2996 -0.110 -0.127 -0.011951  214.408816   6   \n",
       "2015-06-09  3456.79  26.8020 -0.110 -0.126 -0.003327  183.323049   7   \n",
       "2015-06-10  3526.48  25.8610 -0.111 -0.117  0.019960  300.555651   8   \n",
       "2015-06-11  3551.91  26.3897 -0.111 -0.120  0.007185  279.249096   9   \n",
       "2015-06-12  3502.77  29.7725 -0.113 -0.125 -0.013931  302.564935  10   \n",
       "2015-06-15  3438.07  34.5593 -0.108 -0.119 -0.018644  359.901600  11   \n",
       "2015-06-16  3454.09  36.2222 -0.109 -0.125  0.004649  332.134168  12   \n",
       "2015-06-17  3428.76  34.7235 -0.109 -0.110 -0.007360  315.833038  13   \n",
       "2015-06-18  3450.45  34.7235 -0.109 -0.118  0.006306  299.246550  14   \n",
       "2015-06-19  3455.80  34.7235 -0.108 -0.120  0.001549  278.303868  15   \n",
       "\n",
       "                DF_t  F_tS  \n",
       "Date                        \n",
       "2015-06-01  1.000038  3000  \n",
       "2015-06-02  1.000036  3000  \n",
       "2015-06-03  1.000033  3000  \n",
       "2015-06-04  1.000030  3000  \n",
       "2015-06-05  1.000027  3000  \n",
       "2015-06-08  1.000025  3000  \n",
       "2015-06-09  1.000022  3000  \n",
       "2015-06-10  1.000019  3000  \n",
       "2015-06-11  1.000016  3000  \n",
       "2015-06-12  1.000014  3000  \n",
       "2015-06-15  1.000011  3000  \n",
       "2015-06-16  1.000008  3000  \n",
       "2015-06-17  1.000005  3000  \n",
       "2015-06-18  1.000003  3000  \n",
       "2015-06-19  1.000000  3000  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['F_tS'] = 3000\n",
    "\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "data['ARMVM_t'] = 0.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2917"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['F_tS'][1] = data['DF_t'][1] * ((data['t'][1] * data['sigma**2'][1]\n",
    "                    + (T - data['t'][1]) * data['V6I1'][1] ** 2) / T\n",
    "                    - sigma_K ** 2) + 3000\n",
    "data['F_tS'][1]                                      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "for t in data.index[1:]:\n",
    "    data['ARMVM_t'][t] = (data['ARMVM_t'].shift(1)[t]\n",
    "                          * math.exp(data['EONIA'].shift(1)[t] / 252)\n",
    "                       + (data['F_tS'].shift(1)[t] - 3000)\n",
    "                         * (math.exp(data['EONIA'].shift(1)[t] / 252) - 1))\n",
    "    data['F_tS'][t] = data['DF_t'][t] * ((data['t'][t] * data['sigma**2'][t]\n",
    "                          + (T - data['t'][t]) * data['V6I1'][t] ** 2) / T\n",
    "                          - sigma_K ** 2) - data['ARMVM_t'][t] + 3000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SX5E</th>\n",
       "      <th>V6I1</th>\n",
       "      <th>2w</th>\n",
       "      <th>EONIA</th>\n",
       "      <th>R_t</th>\n",
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       "      <th>t</th>\n",
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       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>3575.04</td>\n",
       "      <td>25.8710</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.106</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000038</td>\n",
       "      <td>3000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>3561.89</td>\n",
       "      <td>25.9232</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.122</td>\n",
       "      <td>-0.003685</td>\n",
       "      <td>34.220799</td>\n",
       "      <td>2</td>\n",
       "      <td>1.000036</td>\n",
       "      <td>2917</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>3583.82</td>\n",
       "      <td>25.7958</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.143</td>\n",
       "      <td>0.006138</td>\n",
       "      <td>64.580457</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000033</td>\n",
       "      <td>2875</td>\n",
       "      <td>0.040173</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>3556.38</td>\n",
       "      <td>26.2418</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.138</td>\n",
       "      <td>-0.007686</td>\n",
       "      <td>92.677552</td>\n",
       "      <td>4</td>\n",
       "      <td>1.000030</td>\n",
       "      <td>2860</td>\n",
       "      <td>0.111062</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>3510.01</td>\n",
       "      <td>27.4496</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.115</td>\n",
       "      <td>-0.013124</td>\n",
       "      <td>178.023718</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000027</td>\n",
       "      <td>2892</td>\n",
       "      <td>0.187647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>3468.31</td>\n",
       "      <td>27.2996</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.127</td>\n",
       "      <td>-0.011951</td>\n",
       "      <td>214.408816</td>\n",
       "      <td>6</td>\n",
       "      <td>1.000025</td>\n",
       "      <td>2863</td>\n",
       "      <td>0.236836</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>3456.79</td>\n",
       "      <td>26.8020</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.126</td>\n",
       "      <td>-0.003327</td>\n",
       "      <td>183.323049</td>\n",
       "      <td>7</td>\n",
       "      <td>1.000022</td>\n",
       "      <td>2799</td>\n",
       "      <td>0.305743</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>3526.48</td>\n",
       "      <td>25.8610</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.117</td>\n",
       "      <td>0.019960</td>\n",
       "      <td>300.555651</td>\n",
       "      <td>8</td>\n",
       "      <td>1.000019</td>\n",
       "      <td>2802</td>\n",
       "      <td>0.406065</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>3551.91</td>\n",
       "      <td>26.3897</td>\n",
       "      <td>-0.111</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.007185</td>\n",
       "      <td>279.249096</td>\n",
       "      <td>9</td>\n",
       "      <td>1.000016</td>\n",
       "      <td>2776</td>\n",
       "      <td>0.497784</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>3502.77</td>\n",
       "      <td>29.7725</td>\n",
       "      <td>-0.113</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>-0.013931</td>\n",
       "      <td>302.564935</td>\n",
       "      <td>10</td>\n",
       "      <td>1.000014</td>\n",
       "      <td>2827</td>\n",
       "      <td>0.604188</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>3438.07</td>\n",
       "      <td>34.5593</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.119</td>\n",
       "      <td>-0.018644</td>\n",
       "      <td>359.901600</td>\n",
       "      <td>11</td>\n",
       "      <td>1.000011</td>\n",
       "      <td>2912</td>\n",
       "      <td>0.689681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>3454.09</td>\n",
       "      <td>36.2222</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.125</td>\n",
       "      <td>0.004649</td>\n",
       "      <td>332.134168</td>\n",
       "      <td>12</td>\n",
       "      <td>1.000008</td>\n",
       "      <td>2858</td>\n",
       "      <td>0.730901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>3428.76</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.110</td>\n",
       "      <td>-0.007360</td>\n",
       "      <td>315.833038</td>\n",
       "      <td>13</td>\n",
       "      <td>1.000005</td>\n",
       "      <td>2764</td>\n",
       "      <td>0.800957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>3450.45</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.109</td>\n",
       "      <td>-0.118</td>\n",
       "      <td>0.006306</td>\n",
       "      <td>299.246550</td>\n",
       "      <td>14</td>\n",
       "      <td>1.000003</td>\n",
       "      <td>2689</td>\n",
       "      <td>0.903601</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>3455.80</td>\n",
       "      <td>34.7235</td>\n",
       "      <td>-0.108</td>\n",
       "      <td>-0.120</td>\n",
       "      <td>0.001549</td>\n",
       "      <td>278.303868</td>\n",
       "      <td>15</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2607</td>\n",
       "      <td>1.048771</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SX5E     V6I1     2w  EONIA       R_t    sigma**2   t  \\\n",
       "Date                                                                   \n",
       "2015-06-01  3575.04  25.8710 -0.108 -0.106       NaN         NaN   1   \n",
       "2015-06-02  3561.89  25.9232 -0.109 -0.122 -0.003685   34.220799   2   \n",
       "2015-06-03  3583.82  25.7958 -0.109 -0.143  0.006138   64.580457   3   \n",
       "2015-06-04  3556.38  26.2418 -0.109 -0.138 -0.007686   92.677552   4   \n",
       "2015-06-05  3510.01  27.4496 -0.109 -0.115 -0.013124  178.023718   5   \n",
       "2015-06-08  3468.31  27.2996 -0.110 -0.127 -0.011951  214.408816   6   \n",
       "2015-06-09  3456.79  26.8020 -0.110 -0.126 -0.003327  183.323049   7   \n",
       "2015-06-10  3526.48  25.8610 -0.111 -0.117  0.019960  300.555651   8   \n",
       "2015-06-11  3551.91  26.3897 -0.111 -0.120  0.007185  279.249096   9   \n",
       "2015-06-12  3502.77  29.7725 -0.113 -0.125 -0.013931  302.564935  10   \n",
       "2015-06-15  3438.07  34.5593 -0.108 -0.119 -0.018644  359.901600  11   \n",
       "2015-06-16  3454.09  36.2222 -0.109 -0.125  0.004649  332.134168  12   \n",
       "2015-06-17  3428.76  34.7235 -0.109 -0.110 -0.007360  315.833038  13   \n",
       "2015-06-18  3450.45  34.7235 -0.109 -0.118  0.006306  299.246550  14   \n",
       "2015-06-19  3455.80  34.7235 -0.108 -0.120  0.001549  278.303868  15   \n",
       "\n",
       "                DF_t  F_tS   ARMVM_t  \n",
       "Date                                  \n",
       "2015-06-01  1.000038  3000  0.000000  \n",
       "2015-06-02  1.000036  2917  0.000000  \n",
       "2015-06-03  1.000033  2875  0.040173  \n",
       "2015-06-04  1.000030  2860  0.111062  \n",
       "2015-06-05  1.000027  2892  0.187647  \n",
       "2015-06-08  1.000025  2863  0.236836  \n",
       "2015-06-09  1.000022  2799  0.305743  \n",
       "2015-06-10  1.000019  2802  0.406065  \n",
       "2015-06-11  1.000016  2776  0.497784  \n",
       "2015-06-12  1.000014  2827  0.604188  \n",
       "2015-06-15  1.000011  2912  0.689681  \n",
       "2015-06-16  1.000008  2858  0.730901  \n",
       "2015-06-17  1.000005  2764  0.800957  \n",
       "2015-06-18  1.000003  2689  0.903601  \n",
       "2015-06-19  1.000000  2607  1.048771  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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9EjgCjAlfGhf+/Lj08LEzHRciajRuHGT16gxuuinI/PlOWrSI5ZdfZNFxJGzf\nbpCUFENsrMmUKRKG84PDAW+9lUmHDj62bbPTq1cMIRmZF+K85fjdQmu9UmvdAqiilOqhtf5Ra+0N\nn14D/DP8OBUoke3WkuFjqeHHpx4XIqpUqGCyYEEGXbv60NpO8+axLF4syybz07FjVkHWY8cM3nwz\nE6UkCeQXw4Dhw7O4+eYAH3/sZPhwV6SbJETUuOB3CqVUNaCy1npZ+NAurCCWrLUeHD4WD+wIP14K\nPBu+1wkkAF9ordOVUhlKqTitdSrQKHztOZUtW+LcFwmRzyZOhKZNoUsXg86dPQwcCMOHg1NqW+Yp\n04QBA+Cnn6BvX+jWzRPpJhVJCxZAvXowapSbm25y88gjkW6REAVfTlZHVgGSgU2ACytUJQH9sCbp\npwE1gOe01tvD9wwESmOtjlyutV4SPl4rfO/u8PlBsjpSRLuffrLRqVMM27fbqV8/wKRJmZQrJ7P2\n88rUqU6GDInhxhutgqIu6YiJmF9+sdGiRSxZWfDRRxncdJP0SAohdcKEyGdHj0K/fjEsXuwkLi7E\npEmZNGggW+bktk2bbLRuHUvJklZB1iuuiLq/Z4XOp5/aeeghD2XKmKxYkcGVV8q/iSjaZANvIfJZ\n8eLWdkcvvpjJgQMGbdp4GDdOyljkpgMHrGHfQADGj8+UAFZA/POfQV56KYu0NBsdOng4dizSLRKi\n4JIQJkQeMQzo0cPPRx95KVPGZOjQGDp3juGIdORetGAQevaM4Y8/bAwZ4uPWW6WXsSDp0sVPYqKP\nH36w06ePrJgU4kwkhAmRx+rXt8pYNGgQYMkSJ3fcEctPP8mv3sUYOdLFZ585aNYsQP/+sjF3QWMY\n8OqrWTRqFGDpUifJyTJRT4jTkXcCIfJBuXImH3zgpVcvH9u327nzzljmz5cyFjmxZo2dkSNd/OMf\nId5+24tN/ooVSE4nvPOOl0qVQrzxhlt+3oU4DZmYL0Q+W7zYQb9+MRw9atCli4+hQ7NkRd95+u03\ng2bNinHsGCxZksF118k4V0GntY2WLWPx+WDhwgxuuEH+zUTRIhPzhShAWrcOsHLlMRISgkye7OLe\ne2P580/Zd+9csrKgSxcPBw8aDBuWJQEsSigVYuJEL34/dOjgkZ91IbKRECZEBFx9tcny5Rm0aePn\n22/tNGsWy5df2iPdrALt2WfdbN5sp107P4mJsjF3NLnttiBDh2aRmmqjY0cPGRmRbpEQBUNOirUa\nWHtGbgDKQ93CAAAgAElEQVTcQFXgMa11Vvj800B/rXXZ8OdOYCJWZf3ywJ9a65fD52oDvcPn4oCB\nUqxVFCWmCVOmOHnuOTfBIDz1lI8+fXwyz+kU77/voHdvD9WqBVm+PIPY2Ei3SFwoa2cDN3PmuLj7\nbj8TJ2bKz7koEvJiOHKd1vplrfWzWFXy2wAopZoApYDsye4+4FKt9YtYgetxpVSF8LlZwNNa6+FA\nEOiYw/YIEZUMAzp39rNgQQblypm8/LKbRx+N4fDhSLes4PjxRxuDBsVQooTJ1KleCWBRyjAgOTmL\n+vUDLFrkZMQImQgpxAWHMK21qbUeBqCUcgBXAFopFQe0A0afcksKcFn48SXAH8Bf4e2PYrTWaeFz\nXwGtLvwlCBH96tQJ8cknGSc2Qb799mJs3SrdBEeOWBtze70Go0dnUqVK1C0kEtm4XDBlSiYVK4YY\nMcLNwoWyYlIUbTn+K6+Uao41LLkE2AwMA5469Tm11p8Dm5VS04E5wPTw0GUckH1cMT18TIgiqWxZ\nk3nzvPTrl8Xu3daKsrlzi+6blGlaWz/t2GGjd28frVoFIt0kkQsuu8xk5kwvxYqZJCXF8N//yv9s\niKIrxz/9WuuVWusWQBXgGcAHdAd6AB6l1GClVFWlVF/AqbXuiNXT1U4pdSeQCpTM9pQlw8eEKLLs\ndnj6aR8zZmTgdELfvh6eeMJNVlakW5b/JkxwsmSJkwYNAjz9dBH8BhRi1aqF+M9/vGRmQmKih337\nZMWkKJou+H+zlVLVgMpa62XhQ7uAElrrXuHzlYDOWuvk8Of/APaCNZSplErBGobcqZTKUErFaa1T\ngUbA0vNpQ9myJS602UJElcREaNgQ/vUvmD7dxbZtLt5/HypVinTL8seXX8KLL0L58vDhhw4qVJDf\n+cKmfXv4808YPNigc+fifP45eDyRbpUQ+SsnqyOrAMnAJsAFJABJWutUpVRVrJ6wHsBw4E2sHq7R\nwA9Yk/hLAH3DgawWkATsBkoDg2R1pBAnZWTAkCExzJ3rpFQpk/HjvTRtWrj3SUxJMWjWLJb9+w3m\nz/fSoEHhfr1FmWlCUpL1833ffX4mTMjEkE4xUcicbXWkVMwXooAzTZg508lTT7nx++GJJ3w8/njh\nLGMRCMD993v46isHzz+fSe/eUg+ssMvKgn/9y8PGjQ6efDKLxx+XvUBF4SIV84WIYoYBHTr4Wbw4\ngyuuMElOdtO+vYeDByPdstw3fLiLr75y0LKln169JIAVBW43TJ2ayT/+EeLVV90sXlx0F6OIokd6\nwoSIIn/9BT17evj0UwcVK4aYMsVLrVqFY/ue5csddOzooXLlEKtWHaNkyXPfIwqPrVtttGplFYFb\nvDiDmjULx8+1ENITJkQhUbo0zJnjZdCgLH77zaBVq1hmz3ZGulkXbdcug759Y/B4TKZM8UoAK4Kq\nVw8xfnwmXq+1YjIlRSaHicJPQpgQUcZuh8GDfcyZ48XjgQEDYujf343XG+mW5YzXaxVkTU83SE7O\npHp16QEpqlq0CPD00z7+/NPGo496yMyMdIuEyFsSwoSIUrfdFuSTT45Ru3aQOXNctGwZy6RJTr77\nzhZVdcWefDKGrVvtJCb6eOABKcha1PXt6+P++/18952dAQNiiL4ZM0KcP5kTJkSUy8yEp592M3Pm\nyb34XC6TGjVCXH99kBtuCHLjjUEqVzYL3PL/2bOdDBgQQ+3aQRYvziAmJtItEgVBZibcd18s331n\n5+mns+jXT1ZMiuglJSqEKAJ27jT45hs7mzfb2bTJzg8/2AgETv7ulyplnghlxz9Kl45ce7//3pqI\n7fHAJ58co2LFqPtbJPJQSorBnXfG8scfNqZN89KypfSSiuiUqyFMKWVg7Rm5AXADVYHHwvtBopR6\nGuivtS6b7Z6WQHWsYq23As201kGlVG2gN1bV/ThgoBRrFSJ3ZGbCli02Nm2yn/j49de/z0C46qrQ\niZ6yG24IUqNGCLc779t26BA0a1aMPXtszJmTQbNmUpBV/K8tW2y0bm2tmFyyJIMaNWS+oIg+Zwth\nOS3Isk5rPQxAKbUAaAO8q5RqApQCTiQ7pdRVwN1a6x7hz9/XWh//izsLaKq1TlNKjQA6AlNz2CYh\nRDYxMVCnTog6dUKAVXNr/36DzZttfPedFco2b7Yzf76T+fOtFZb5MYwZCkGfPh727LHx+ONZEsDE\nGdWsGWLs2Ew6dfKQmOhhxYoM4uKkx1QUHhc1HKmUcgDrsTbu/h14HngN+FZrHRe+ZghWL9dvWFsT\nfaq1/jS8/dEKrfU14evuA9prrdue48tKT5gQucQ0rWHM46Fs0yY7W7fa8PvzbhjzrbdcDBvmpkmT\nAO+958Vuz4UXIgq1N990MXy4mzp1gsyfn5EvvbVC5Ja86AlDKdUcGAAsATYDk4CBWD1h2VUCrtFa\nD1RKuYDNSql7gMuA7GkqHSusCSHyiWFA1aomVasGaNfOmnOTmQk//HByGPO77+ysWeNgzZqTfy5y\nOoz5xRd2Xn3VxeWXWzWhJICJ89G/vw+tbcyf72TgwBjGjJE9JkXhcNET85VS04HtQAVObsTdG3gJ\nmA90AYJa66fD178LLAfWAiu11leHj593T9hFNVgIccHS0uCbb+Drr62PjRv527ZJLhdcdx3UrQv1\n6lkfV1/N394o//gDrr/emg/2xRdQv37+vw4RvbxeuPVW62fvtddg8OBIt0iI85Z7PWFKqWpAZa31\nsvChXUAJrXWv8PlKQGetdXL489VAp2xPUQnQWuudSqkMpVSc1joVaAQsPZ82yHCkEPmvTh3ro0+f\nk8OY2Sf9b95sY+NGg7FjretPHcZ88003aWl2hg/PpGpVP2lpkX09Ivq8845B8+axPPmkQYUKXu68\nU+YTioKvbNkSZzyXk9WRVYBkYBPgAhKAJK11qlKqKtAj/DEceFNr7VVKPYdVGLY4kKa1fi38XLWA\nJE72oA2S1ZFCRKdThzE3bbKze/ffV2Ped5+fCRNkKEnk3PffWysmbTZYujSDa6+VFZOiYAkGrUVQ\nKSnWx8MPx0qdMCFE/jtw4ORqzMOHDZ56KovixSPdKhHtFi920Lmzh4oVQ3z8cQaXXRZ172N/4/NZ\nQ/qiYAsEToarffsMUlJsJx6nptrCxwzS0gyCwZO5yzTPPBwpIUwIIUTUGTHCRXKym3r1AnzwgTeq\nVkyGQlaP3sqVDlatcvDf/9qpWzdAv34+mjULSk9xPgsEIC3NOBGiUlJs4WBlsG/fyaC1f79BKHTm\nf5yYGJNy5UzKlQtRvvzxxyYvveSWECaEEKLwME3o1i2GhQudPPSQn7feKtjD3MeOwRdfOFi1ys6q\nVQ5SUqyheofDJD4+xLZt1lLhatWCJCX5uOeeAI4c1y8QAH4/pKYe77mynRgezB60UlKscGWaZ/7h\n8XhOH66yf16+fIiSJTntz6BsWySEEKLQyciAe++N5f/+z87QoZn06uWPdJP+5rffDFatsnq71q61\nk5VlvReXKRPittuC3HFHgCZNApQsCVu32hgzxsWCBQ5CIYOKFUP07u3joYf8sqfqWezebfDpp44T\ngSp72Dpw4OzhKjb2ZIA6fbCyPi9R4vTh6nxJCBNCCFEo7d1rrZhMTTWYPdsb0R0YgkHYtMnGqlUO\nVqxw8OOPJwvhXXttkObNA9x+e4AbbgidsUbe7t0G48a5ePddJ1lZBmXLhuje3c+jj/ooWTKfXkgB\n5/fDihUOZsxw8tln/9tdWKzYyQBVvrxJXNzfg9bxx8WLX1y4Ol8SwoQQQhRamzfbuOeeWBwOWLYs\ng4SE/FsxeeQIfPaZg5UrHaxebWf/fmuY0e02adw4yO23W8HrH/+4sPfalBSDSZOcTJ3q4sgRgxIl\nTDp18tG1q7/Ibt30228Gs2Y5mTPHeWI4t149q9B01aohypU7Ga4KEglhQgghCrUFCxx062atmFyx\nIoMyZfLuvW3XLoOVK63gtWGD/cQ2X3FxoXBvV5Cbbw7kShhIT4dp01xMmOBk/34bMTEmDz3kp1cv\nH5UqRd379wULBOCTT+zMmOFi9Wo7pmlQsqTJAw/4SUz052vgzikJYUIIIQq9V1918cYbbho0CPD+\n+95cK/sQCMDGjfbwakY7v/xyciyxdm2rt6t58wC1aoWw2c7yRBfB64X33nPy9tsu9uyxYbeb3Htv\ngKQkH9WqFfwgcqH+/NNg9mwns2c7+fNP65t6441BOnb0cffdAWJjI9zAC5CrIUwpZQCLgQ2AG6gK\nPKa1zgqffxror7Uue8p9CcBG4MHj1faVUrWxtjjahbVv5EAp1iqEECInQiHo0iWGJUucPPKIj5Ej\ns3I85+fgQVizxppUv3q1g8OHrSfyeEyaNLF6u26/PUD58vnbkREIWL1+Y8a4Tsw5a948QFJSFnXr\nRncYCwbh00/tzJjhZOVKa4FC8eImbdv66dDBT40a0fn68mID73Va62EASqkFQBvgXaVUE6wNvP/2\nU6mUigGeAL4/5XlmAU211mlKqRFAR2BqDtskhBCiCLPZYMyYTH791casWS4SEkJ063Z+KyZNE375\nxcbKlVYJiY0b7ScKbl5xRYj77vNzxx0BGjYM4vHk5as4O4cD2rYN8K9/BVi1ys6oUe4TQ6P161u1\nxpo2ja5aYykpBnPmOJk1y8lvv1m9XtddF6RDBz/33usvcHO8ctMFhzCttQkcD2AO4ApAK6XigHbA\na0CHU257BXgRmHb8QHj7oxit9fEd5L4C2iMhTAghRA4VKwYzZ3pp3jyW555zc/XVIZo2Pf2KSZ8P\n1q+3QtfKlY4T22wZhskNN4S44w5rUv2114YKXKgxDGjePEjz5hls2GBn9GgXn3ziYMMGB9WrW7XG\nWrcuuLXGQiH44gs706c7WbHCQSBgEBtrkpjoo0MHP7VrR2ev14XK8T+PUqo5MABYAmwGJgEDsXrC\nsl+XCHyptf5VKZX9VByQfVwxPXxMCCGEyLHLLzeZPt3LvffG0rWrh+XLM4iPt97U09IMVq+2gten\nnzo4etRKV8WLm7Ru7ef22wPcdluQsmWjZ750/fpB6tf38sMPVq2xhQsddO/uYfhwq9bYAw8UnFpj\naWkG777rZOZMJ7/+aoXe6tWDdOzo51//8lPizHtdF0oXPTFfKTUd2A5U4ORG3L2Bl4D5wL8BDRhA\nN+BzYBHW0ORKrfXV4ee5D2ivtW57ji8ZPb8ZQgghImbOHGjfHqpWhccegyVL4OuvraFHgCpVoHVr\nuOsuuOWWwrN/444d8PrrMHWq1dtXvjwMGAA9ehCRWmOmCZ99BhMmwEcfWXW+PB548EHo3h3q1s2f\nel0RlKsT86sBlbNNrh8KxGqtB4c/rwR8e+rE/PC5T4HXs937PdBMa50anhO2VWt9ruFImZgvhBDi\nvAwb5uKtt6yNJe12k7p1j69mDHLNNQVvmDE3paQY/Oc/TqZNc3H0qMEll1i1xrp08edLT9+BAwZz\n5zqYOdPFjh1Wr1dCgtXr1batn0suyfMmFAi5vTqyCpAMbAJcQAKQFA5SVYEe4Y/hwJtaa2/4vgFA\nH2AtMF5rvUEpVQtI4mQP2iBZHSmEECK3hEIwbZqTSy4xado0QKlS576nsDl8GKZOdTFx4slaY+3b\n++nZ00fFirkbxkwTvv7azrRpTpYsceDzGbjdJnffHaBDBz9160bXooHcIHXChBBCiCIuIwPefdfJ\nuHEufvvNqjXWpk2Avn19F1309NAhmDfPyYwZTn7+2SqdcfXV1grHdu38lC6dG68gOkkIE0IIIQRg\nzck6Xmvsp5+swHTnnX6SknzcdNP5hzHThG+/tTFjhrUYIDPTwOk0ad3a6vVq0KDo9XqdjoQwIYQQ\nQvxNKMSJWmPffmuFsYYNrSr8//znmQNUejq8/77V63W8YGzlyiESE308+GCAyy6LulyRpySECSGE\nEOK0TNOqlzZ6tIs1a6zKVTVrWrXG7rorgN1uXfN//2djxgwnH33kJCPDwOEwadEiQMeOfho3DubZ\nlk3RTkKYEEIIIc5pyxar1tiiRda2QZUrW7sFrFrlYMsWq9erYsUQiYl+HnzQT7lyUZch8p2EMCGE\nEEKct507Dd5+28XcuU58PgO73aR5c6vX69ZbpdfrQkgIE0IIIcQF27fP4Kuv7DRsGKRChajLCwWC\nhDAhhBBCiAg4Wwi74L0jlVIGsBjYALiBqsBjWuus8Pmngf7HK+YrpVoA9wNbgVrAh1rrReFztbG2\nONqFtW/kwPMo1iqEEEIIEfVyOqq7Tmv9stb6WSAWaAOglGqCtYF39u61K4FntdYjgSeAGdnOzQKe\n1loPB4JAxxy2RwghhBAiqlxwT5jW2gSGASilHMAVgFZKxQHtgNeADtmun5TtdjtwNHxvFSBGa50W\nPvcV0B44196RQgghhBBRL8frG5RSzbGGJZcAm7GC2VPneM4nsPaPBGv4MfvkrvTwMSGEEEKIQu+C\ne8KO01qvBFYqpaYDzwA+oDvWRtwepdRgrPlfOwCUUgOB77XWC8JPkQqUzPaUJcPHzsUoW7ZETpst\nhBBCCFEg5GRifjWgstZ6WfjQLqCE1rpX+HwloLPWOjnbPc8AWmv9fnje2Pda651KqQylVJzWOhVo\nBCy92BckhBBCCBENctITlgV0UkpdB7iABCAJQClVFegBxCilngLeBLpgDUFuU0r1Ai4HbgcOAo8A\nw5RSu7GGMadfzIsRQgghhIgW0VgnTAghhBAi6snGA0IIIYQQESAhTAghhBAiAiSECSGEEEJEgIQw\nIYQQQogIkBAmhBBCCBEBEsKEEEIIISJAQpgQQgghRARICBNCCCGEiAAJYUIIIYQQESAhTAghhBAi\nAiSECSGEEEJEgIQwIYQQQogIkBAmhBBCCBEBEsKEEEIIISJAQpgQQgghRARICBNCCCGEiAAJYUII\nIYQQESAhTAghhBAiAhy5/YRKqRjga2CF1nqwUqoU8CqwE7gaeEprnRa+dhBQErgUWKW1Xpzb7RFC\nCCGEKIjyoifsZWBTts+HYQWs14CFwEgApVRd4Fat9XPAAGCkUqpEHrRHCCGEEKLAydUQppR6BFgL\n7M52uBWwPvz4K6Bl+PFdx49rrYPAj0CT3GyPEEIIIURBlWshTClVDUjQWi845VQccCT8OB0opZSy\nnXL8+Lm43GqPEEIIIURBlps9YfcBmUqpIUBjoK5Sqh+QAhwfZiwJHNRah4DUbMePn0vNxfYIIYQQ\nQhRYuTYxX2s97Pjj8OT8YlrrUUopBTQAPsAKZ0vDly0Fng1f7wQSgC/O9XVM0zQNw8itZgshhBBC\n5KUzhhbDNM1c/UpKqTZAL8AFvA2sAF4D9gBVgCezrY4cCJTGWh25XGu95Dy+hJmWduTcVwkhhBBC\nRFjZsiXyL4TlAwlhQgghhIgKZwthUqxVCCGEECICJIQJIYQQQkSAhDAhhBBCiAiQECaEEEIIEQES\nwoQQQgghIkBCmBBCCCFEBEgIE0IIIUSB07VrB/KyjNaRI1a5q2HDXgAgPT2djIwMAoEAU6ZMZN++\nvaSnpwMwZswbTJo0npkzp/H8809x8OBfudIGqRMmhBBCiCJn06Zv+fzzNezYsZ26desTDAZp3LgJ\nixZ9RGrqPqpUuZpgMEDXrr2YPv0dunTpAcDs2dNJS0ujf/9B5/V1zlYnLNe2LRJCCCFEdBs61M3i\nxbkbDVq3DjB0aNZZr9mz51dmzZrG1Vdfw88/axISqjF37hzGjJlI+fLlWb58CevWrSU+XrFjx3b+\n+usA/fsPYvr0d9i7dy916tTjhx+2cPPNt3Do0CF+/lmjVAKdO3cH4KmnnqBateqkpaVQo0Ztmje/\nk4oVr+Kyy8qyadO3HD16hAYNGlOuXHmuvPJKtm/XHDp0iEaNbsbpdJ4IYAChkInH48mV702ufqeV\nUgawGNgAuIGqQCdgCNAk26WvaK1Xh+8ZhLV596XAKq314txskxBCiKLJNOHLL+1Mn+6keHGoWzdI\nvXoBqlY1kS2IC5YNG9bhcrlo06Yd+/en4XQ6+eKLzwBITz/MhAlj+PDDpTgcDiZNGk+lSldRpcrV\n9OyZRN++3enatSdHjx7l3nvvZOnST3C53LRt2/pECGvZsjWNG99CKBSiffu2NG9+J/v27aVFi7v4\n7bc99OyZxJdffs6BA/tp0KARGRkZtGjRmu3bNaFQCJvNmr115MgRvv32a15+OTlXXnde9IStO76Z\nt1JqAdAGMLXWTU+9UClVF7hVa32XUsoO/KiU+kxrLeONQgghciQUgpUr7bz1lptNm+wnjr/7rhOA\nMmVC1KkTDIeyILVqhXC7I9XagmXo0Kxz9lrlhbvvvo9Zs6bRq1cXKlW6ij59+p+YD/bHH79TqlQZ\nHA4rslx++RWkpqacuLdChcsBKF68OKVKlcHtjgHAZrP+7YPBILt27eTnn3/C5XJx+PBhAGrUqAnA\ngw8+gmEY3HLLrSees3nzFpQuXZrGjU/2Hx07dpS33krm3/9+nhIlSuTK687VEKa1NoHjAcwBXAH8\nBFyjlHoKyALswBittRe4C1gfvjeolPoRq8fsfDbyFkIIIU4IBmHRIgdvveXixx+tN+CWLf0kJflw\nu+Hrr+1s3Gh9fPyxk48/tkKZ221y/fUnQ1mdOkEuvTSSr6To2bbtBxITH6VLlx6MGzeKjz9eeuLc\nFVdcycGDf+H3+3E6nfz55x8nAplpmqdM3v/fx+vXr+Xbb79m1KjxAHz44by/fe0qVar+T3uuuOLK\nv31+6NAhxowZSc+e/bjsssv4/PM1NGnyP31LFyxP5oQppZoDA4AlWutNSikvsFtr7VVK9QTGAF2A\nOGBbtlvTw8eEEEKI8+LzwQcfOBg92s3OnTZsNpN//ctPv34+EhJCJ66rXj1Ep05+AP74wzgRyI6H\nsw0bTr4lJiRYYaxePSucVaokQ5h5KT39MKNHv8Hll1/BoUOHqFDhClJS9rFw4Yd0796b7t178/zz\nT1GtWnX++usvypUrB8CSJQtJSdnH5s3fsW/fXo4dO8aXX36GacKxY8dYsmQhjRrdwrx57/LWWyMo\nW7YsmZmZLFu2mJYtW593+x5/vA+hUIgXX3wG0zQpVqxYroSwPF0dqZSaDqzXWk/IdkwBy7TWVZVS\nLwJZWutXwucWApO01mfrCYu65ZxCCCFyn9cLkyfD66/Db7+B0wmPPgpDhkDV/+3cOKv0dPj6a1i7\n1vrYsAEyMk6er1ABGjWCxo2t/153HThkaVu+2bJlCzVrWsOHb7/9NhUrVqR16/MPURF2xvieqyFM\nKVUNqKy1Xhb+fCgQC6C1Hhw+1hroq7VurpSqBzwbnhPmBH4A6mit08/yZaREhRBCFGFHjsDUqS4m\nTHCyf78Nj8ekQwc/PXv6uPzy3HlP8/th61bb33rKUlJOltaMjTW58Uarl6xu3SA33RQkl6YJidMY\nM+YNYmOL4XK5OHDgAElJj5+YLF/Qna1ERW6HsCpAMrAJcAEJQBLQDyuMpQE1gOe01tvD9wwESmOt\njlx+jl4wkBAmhBBF0l9/waRJLiZPdnH4sEGJEiadO/vo1s3PZZfl7SCJacKePcbf5pX99NPJSf82\nm8m114ZODF/WqxfMtUAoolu+hbB8IiFMCCGKkJQUg3HjXEyf7iQjw6BMmRDduvnp1MnHJZdErl0H\nD8K3356cV7Z5s52srJPvt1deGTrRU1avXpCEhBB2+1meUBRKEsKEEEJEnT17DMaOdfHuu06ysgzK\nlw/Ru7ePRx7xU6xYpFv3v7Ky4PvvTw5hfvONnQMHTg6ZlShh/q00xvXXB4mNjWCDRb6QECaEECJq\n/PKLjdGjXXzwgYNg0KBSpRB9+/p44P/Zu+/wqKr8j+PvqSlIBCQUkSp4RAUUpUkVKwsqYl0bq4gr\nsiIr6E+xrLu6dl1cBWyIru7aURRFFFQ6UhWUcJQmTSkiNWXa/f1xJwWQFpJMJvm8nmeezNy5d+ZM\nYDKfOed7z7k8nFTzeTkOLF/uiYcyP3Pm+Fi+vDCU+f0OLVu6c5YZE6NhwxiNGsU4+mhHPWYViEKY\niIiUe4sXexk+PMj48X4cx4MxUQYNCnHRRZEKcybipk0e5s4tLPZftMhLOLz7Z3Qw6FC/vkOjRrE9\nLg4NGsQooRVzpIwohImISLk1Z46X4cNTmDTJTVqtWkUZPDhEjx4RkuQEuGLLyYFFi3ysXOlh1Spv\nkYuHLVt+/8XXrZvfa7Z3UKteHc1nVs4ohImISLniODB1qo/hw4PMmOGGr/btIwweHOKMM6IKErhz\nlxUNZj/9VBjU1q3zEIvt/UvKyNgzmDka5kwwhTARESkXYjGYONHP008HC9Z17N7dDV/t20cT3Lrk\nEQrBmjV79p65PWg//eQlN3fvz30NcyaGQpiIiCRUNArjxrnhK39dx549wwweHKJVq9gBjpZDEYu5\n03oUDWUa5kwchTAREUmIUAjeeSfAv/8dZOVKLz6fw0UXRbj11hDGKHwlwp7DnKtW7T7M6Th7Z4Yj\nj3SDWePGsSI/HRo3jlG7ttbV3B+FMBERKVPZ2fDf/wYYMSLI+vVegkGHyy8Pc8stIRo1SrrPnUoj\nL88d5izae7ZyZWGPWtHJaPOlpbkBrWHDGI0bO7sFtXr1nApzZmtxleWyRR7gI2A2kAIcC1yHu2TR\nI8AKoCkwzFq7KX7MUCADd9miz621Hx3gaRTCRETKqT3XdUxPd7jmmjA33xyibl2Fr2QWi8HPP3t2\nC2buT/f2zp17Zw2/36FBg9/vRWvQIEZqagJeSBkr6xB2l7X2ofjtD4C3gc7AZGvtu8aYXsBl1tpr\njTFtcdeR7GWM8QFZwKnW2v2lLIUwEZFyZssWeOGFIKNHF67reMMNIfr3L/11HSXxHAc2b/bsFczy\nhzuLrhyQz+NxqFevMKA1bLh7L9oRRyTghZSC/YWwEu0ktNY6QH4A8wP1gKW4vWAPxnebAbwSv94L\nmNmVMd4AACAASURBVBU/NmqMyQK6AgdaxFtERBIoFAJrvSxa5GPBAi/vvVe4ruOwYSGuvz5ERkai\nWyllxeOBzEyHzEyHNm32rvUrWodWtBdt5Uov06f7mT5978esWdMd3tyzF61x44pzokCpjNQaY84B\n/gqMt9YuMMbUAvK7r7YD1Y0xXqAWsKTIodvj20REykQkAnfemcKnn/o59tgYJ5zgXpo3dxdcrijf\nxg9HTg4sWeIGrsWL3Z9ZWbvP9F63boxhw/K4+uqw1kOUvWRkQMuWMVq23Dug5eQQr0HbuxdtwQIv\nc+fuPblZRkZhr1mTJjGOPz5G8+Yxjj02RiBQFq+oZJRKCLPWfgZ8Zox51RgzANgAVMUNWRnAb9ba\nmDFmY3x7vgxg44EePzOz6oF2ERE5oHAYrr4a3n4bqlWD2bO9zJq1+z7HHgstWkDLlu6lRQt3W0Wd\n9HLHDvjmG1iwoPCSleVOMZEvJQVOPhlaty68tGrlJRBIBSpBkY+UuAYNoHPnvbeHw7B6NSxbBsuX\nuz/d6x6WLvXx7be7vxEDATj+ePd9WvRSv3757Dkr0RBmjGkONLbWfhLftBJoDHwMdADeBTrFbxP/\neW/82ABwPDD1QM+jmjAROVyhEPz5z6l8/HGA9u0j/O9/OXi98MMPXpYs8bJkidvbs2SJlw8+8PLB\nB4XHpqU58W/e0SI9ZzGOOiq5ap+2bIHFi30sWuTju+/cHq6iC0wDpKc7nHZalJYtY7Ro4f5s1mzv\n3oatW8uw4VKpZGQUhv2iYjH45RcPy5Z5ycpyL0uX+li61MvixZ49HsPh+OOjBT1m+b3d1aqVfvv3\n13FU0oX5TYDHgAVAEDdUDQLCuHVhq4EmwJ1Fzo4cAtTAPTtygrX2QPVgKswXkcOSlwf9+6fy6acB\nOnWK8NprOVSp8vv7Og5s3OiJBzMvWVk+lizx8sMPXkKh3f/Q165dGMhOOCFK8+YxjjsuRkpKGbyo\nA9iwwVMwlLhokZfFi32sWbN74DrySIeWLaO0aBGjZcsoLVtGadxYS91IconF4KefPGRl+QrCWVaW\nlxUrvESju79n69Z136/uxX3PNmtWsmdtap4wEZG43Fy4/vo0Jk3y07VrhFdfzSlWDVM4DCtWePcK\nZ2vX7h5sfD6HZs0Kv33nh7N69UpngkvHgXXrPLuFrUWLvGzYsHu7ataMxWt0CkNXgwaadFMqrtxc\n+PHH/FBWGNB+/nnv92yTJkXDmRvQGjZ0irWgvEKYiAhuAXDfvml89ZWf7t0jvPJKTonPU7RtGwWB\nLD+cZWXtPYdSRoZTEMjyh0aaN49R9RBKXmMxWLXKUxC08gvn91yWpl49dyixsIcrRp06ClwiAL/9\nBkuX7t5rlpXlY8eO3d8g6ekOxhS+V/MvmZn7z1EKYSJS6WVnwzXXpDFtmp9zzokwenROmQ0TOo47\nC/metWbLl3uJxXb/+9yggdtbVrTWrHFj94yyZcu8u/VuLV7s2yvcNWpUGLTyg5fm6RI5NPk9ynv2\nmv344+5nBYPbq7xnr5kxsYISB4UwEanUdu6Eq69OY+ZMPz16hHnxxVyCwUS3yu2Z+/FHN5B9/31h\nONu8efeerNRUt9cqJ6fwb7nH4w5zFu3dOumkKEceWdavQqTyCIdh+fLde8yysrysXr37e9bjcWjY\n0KF58yiffBJQCBORymnnTvjjH9P4+ms/558f5rnncsv9PEIbN3oKAln+0GY0SpEaLrenTHOYiZQP\nO3fC0qXevU4G2LLFi+OgECYilc/27XD55enMn+/joovCjBiRW+kXExaRspF/ZvVJJx2xzxBWjDp/\nEZHyb+tWuPRSN4BdcokCmIiULY8Hatfef0eXQpiIVDhbtsAll6SzcKGPK64I88wzCmAiUv4ohIlI\nhbJ5s4eLL05n0SIf11wTYvjwXE02KiLlUkkvW9QEeBCYD9QHfrXWPmCM+RvQtciu/7TWTo4fMxR3\nzchqwOfW2o9Ksk0iUnls2uThkkvSyMry8ac/hXjkkbxiTa4oIlIWSrqDvgbwRn6QMsZ8b4wZDzjW\n2u577myMaQt0s9b2Msb4gCxjzFfWWlXei8gh2bDBw8UXp/HDDz769w/x4IN5moxURMq1Eg1h1tp5\ne2zyALsAjzFmGJAH+IBnrLU5QC9gVvzYqDEmC7fH7EDrR4qIFPj5Zw99+qSzfLmXAQNC3H+/ApiI\nlH+l1lFvjOkNTLTW/gC8DfzLWvsksAN4Jr5brfjtfNvj20REDsq6dR4uvNANYIMG5SmAiUjSKJXz\nhYwx3XCHGQcDWGuzitz9BTA0fn0jUHSltIz4NhGRA1q92u0BW73ay2235fF//xdSABORpFHiIcwY\n0xPoZK0dbIypCzQE+lhr74jvchywPH79Y+De+HEB4Hhg6oGeIzPzEFa4FZEKacUK6NMHVq+Gv/8d\n7rsvBSijxSBFREpAic6Yb4xpDUwB5uLWg6UDIwATv74JOAm4z1q7LH7MENyC/mrABGvtgerBNGO+\nSCW3YoXbA7Z+vZdhw/IYPDiU6CaJiPwuLeAtIhXGsmUeLroonQ0bvNx3Xy5/+Us40U0SEdmn/YUw\nzSEtIknDWi99+qSxaZOXf/wjl5tuUgATkeSlECYiSWHJEi+XXJLG5s1eHn44l379FMBEJLkphIlI\nubd4sZdLL01jyxYvjz+eS9++CmAikvwUwkSkXPv2Wy+XXprOtm3wr3/lctVVCmAiUjEohIlIubVg\ngZfLLktnxw54+ulcrrgikugmiYiUGIUwESmX5s71csUV6ezaBSNH5nLxxQpgIlKxKISJSLkze7aP\nP/4xjdxceP75XC68UAFMRCqeUls7UkTKTm6uW7yek5Polhy+GTN8XHFFGnl58MILCmAiUnGpJ0wk\nSW3e7GHSJB+ffurnq6/8ZGd7SElxaNMmSqdOUTp3jnDKKTH8SfQunzLFx7XXphGJwOjRufTooQAm\nIhVXSS9b1AR4EJgP1Ad+tdY+YIypDjwCrACaAsOstZvixwzFXbi7GvC5tfajAzyNZsyXSslxYNky\nL59+6mfiRB9z5/pwHHci5qZNo7RpE2PxYi/ffecrOOaIIxw6dHADWadOUU44IYa3nPZ/f/GFjz/9\nKY1YDMaMyeHss6OJbpKIyGErs2WLjDGnAXXzg5Qx5nvgauBGYLK19l1jTC/gMmvttcaYtrjrSPYy\nxviALOBUa+3+UpZCmFQakQjMmeP2dn32mZ8VK9wE5fU6tG0b5dxzI5x7boSmTQvfx7/+6mHmTB9T\np/qYNq3wGICjjorRqVNhT1njxg6eff55KDuTJrkBzOuFV17JoXt3BTARqRjKbNkia+28PTZ5gF1A\nT9weMoAZwCvx672AWfFjo8aYLKArcKBFvEUqrB074Msv/Xz6qZ9Jk/xs3eq+f9PTHXr1CnPuuRHO\nOivKUUf9/heoo45yOP/8COefHwHyWLfOw7RpPqZP9zNtmo9x4wKMGxcAoF69GJ07R+nUKUKXLlHq\n1Cn7tWQ//dRHv35p+P3w2ms5dOmiACYilUOpVYsYY3oDE621PxhjagH53VfbgerGGC9QC1hS5LDt\n8W0ilcratR4mTvQzcaKfGTN8hMNu8KpbN0bv3mHOOy/C6adHSU099MeuV8/hiisiXHFFBMeB5cs9\nTJvmBrIZM/y8+WaAN990Q1mzZvm9ZFE6doxQvXpJvsq9jR/v58YbUwkG4b//zaFjRwUwEak8SiWE\nGWO6Ad2stYPjmzYAVXFDVgbwm7U2ZozZGN+eLwPYeKDHz8yseqBdRMo1x4EFC+DDD93LN98U3nfK\nKXDBBe7llFO8eDxBIFhiz12rFnToAHfcAbEYfPstTJ4MX3wBU6f6GDPGx5gx4PG4bTnzTOjeHTp3\nhipVSqwZvP029O8PaWkwYQJ06pRecg8uIpIESjyEGWN6Ap2stYONMXWBhsDHQAfgXaBT/Dbxn/fG\njwsAxwNTD/QcqgmTZJSbC9OnF9Z3/fKLW6sVDDp07+7Wd51zToR69QqHBDdvLv12HXMM9O3rXkIh\nWLjQFx++9DFvno8FCzw8/jgEAg6tW7u9ZF26RGndOkqwmNnwvff8DByYSpUq8Oab2RgTY9Omkn1d\nIiLlwf46jkq6ML81MAWYi1sPlg6MAD4EHgVWA02AO4ucHTkEqIF7duQEa+2B6sFUmC9J4/emkQCo\nXt3h7LPdovozzohwxBEJbug+ZGfD11+7gWzaND/ffustOCMzPd2hXTt3+LJLlwgnnRTD5zvAAwJv\nveXn1ltTqVoV3norm9atY6X8KkREEqfMzo4sIwphUm7tbxqJJk1inHdehPPOi3DaadGkmr8r39at\nMHOmv6CnzNrC1FWtmsPpp0fo3NntLWvWLLbXmZf/+5+fv/41lSOPhHfeyaZVKwUwEanYFMJESlEk\nAnPn+uLBa/dpJNq0cYcZzztv92kkKooNGzzxXjK3p2zNmsLpMGrXjhX0knXuHGXyZD+3355KjRox\n3nknhxYtFMBEpOJTCBMpYfnTSEyc6E4j8dtvhUN03bu7tV1nnRWlZs2ke38Vm+PATz+5Z17mB7PN\nm3efGbZmzRjvvpvDCScogIlI5aAQJlIC1q3zFPR27TmNRH5vV3GnkaiIHAeWLvUWDF3+8ouXZ57J\nxRgFMBGpPBTCRA5DOAxPPhlk+PAgsZj7XmrRonCYsUWLvWufREREoAxnzBepaFau9HDzzWnMn++j\nQYMYAwbkcd55u08jISIiUhwKYSK/w3HcqRTuuiuVXbs8XHxxmEcfzSUjI9EtExGRikIhTGQP27bB\n7ben8sEHAapWdRg5ModLLokkulkiIlLBKISJFDFrlo+BA1NZu9ZLmzZRRo7MoWFDDT2KiEjJ8x54\nF5GKLxyGhx8OctFFaaxf7+H22/MYNy5bAUxEREpNifaEGWNqAw8Cray1bePb/gZ0LbLbP621k+P3\nDcVdtLsa8Lm19qOSbI/Iwdiz+H7kyBzattU0CiIiUrpKejiyIzAOaFVkm2Ot7b7njsaYtkA3a20v\nY4wPyDLGfGWt1fwTUib2LL6/5JIwjzyi4nsRESkbJRrCrLVjjTFd99jsMcYMA/IAH/CMtTYH6AXM\nih8XNcZk4faYHWgBb5HDtmfx/ahROVx8sYrvRUSk7JRFTdjbwL+stU8CO4Bn4ttrxW/n2x7fJlKq\nZs3yccYZVfjggwBt2kT54otdCmAiIlLmSv3sSGttVpGbXwBD49c3AlWL3JcR33ZAmZlVD7yTyB7C\nYfj73+Hhh93b998Pd9/tw+8/IqHtEhGRyqm0QljBFP3GmMestXfEbx4HLI9f/xi4N75PADgemHow\nD65li+RQ7av4/rffEt0yERGpyPbXcVSia0caY7oA1wLnAqOAp3CDVjqwCTgJuM9auyy+/xCgBu7Z\nkROstQdTD6a1I+WgqfheREQSSQt4S6W0dSvccUdh8f1jj+Wq9ktERMqUFvCWSmfWLB8335zKunWa\n+V5ERMonzZgvFUrRme9//lkz34uISPmlnjCpMFau9DBgQBoLFmjmexERKf/UEyZJz3HgzTf9dO9e\nhQULfFxySZgvv9ylACYiIuWaesIkqe1ZfK+Z70VEJFkohEnSKlp837ZthJEjc2nQQLVfIiKSHDQc\nKUmnaPH9L794uOOOPD74IEcBTEREkop6wiSp7Fl8P2pUDm3aqPZLRESST4mGMGNMbeBBoJW1tm18\nW3XgEWAF0BQYZq3dFL9vKO6akdWAz621H5Vke6Ti2HPm+0svdWe+r6plREVEJEmV9HBkR2DcHtse\nwg1Yj8bvexLAGNMW6GatvQ/4K/CkMUYfqbKXrVvhxhtTGTQoDa8XnnsuhxEjFMBERCS5lWgIs9aO\nBfZcU6gnMCt+fQbwh/j1XvnbrbVRIAvoWpLtkeQ3a5aPM86owrhxAdq2jfDll7vo00dnP4qISPIr\ni8L8WhQGs+1AdWOMd4/t+ffVKoP2SBIIh+Ghh4L07q3iexERqZjKojB/A1AVN2RlAL9Za2PGmI3x\n7fkygI1l0B4p51R8LyIilUFphbCiK4Z/DHQA3gU6xW/nb78XwBgTAI4Hph7Mg2dmqhioInIcePVV\nuOUW2LkTrrkGnn3WS0ZGlUQ3TUREpMR5HKfkhneMMV2Aa4FzgVG4RfjpuGdHrgaaAHcWOTtyCFAD\n9+zICdba8QfxNM6mTXuWnUmychz46ScPX3/tY/z4ABMn+qla1eHxx3NV+yUiIkkvM7OqZ1/3lWgI\nKyMKYUksGoUlS7zMnu3j66/dy4YNhaWJmvleREQqkv2FME3WKqUqOxsWLCgMXPPm+di5s/D/Y61a\nMS64IEy7dlHato3SokUMr9ZxEBGRSkAhTErU5s0e5sxxA9ecOT6+/dZLJFIYupo1ixYErnbtojRq\n5ODZ53cEERGRikshTIrNcWDVKk9BL9fXX/tYtsxXcL/f79CqVawgcLVtG6VmTQ0zioiIgEKYHIJI\nxK3n+vprX0FN18aNhWOHVao4dOsWoX17N3SdckqU9PQENlhERKQcUwiTfdq1CxYuLAxc8+b52LWr\ncOywdu0YF17o1nO1axelefMYfv2PEhEROSj6yJQCmzbtXs+1aNHu9VzHHbd7PVfDhqrnEhERKS6F\nsErKcdyZ6fND177qufJ7udq2jXLUUarnEhERKSkKYZVENArff7/7/FxF67mqVnXo3j1SELpOPln1\nXCIiIqVJIayCisUgK8vL9Ok+ZszwMWuWn23bCscO69SJ0bt34fxcJ5wQw+fbzwOKiIhIiSqzEGaM\nmQXk4K4rGbHWnm2MqY67pNEKoCkwLH9JIzk0jgM//FAYumbO9LFlS2FPV8OGMXr1CtO+fZT27aM0\naKB6LhERkUQqy56wCdbaf+yx7SHgc2vtu8aYXrhrTV5bhm1KWo4DK1Z4mD7dz4wZbvDatKkwdB1z\nTIyzzw7TsWOETp2iHHOM6rlERETKk7IMYS2NMbfjLug911r7CdATeDB+/wzg1TJsT1LJX+h6xgx/\nQW/XL78Uhq7atWP06ROmU6coHTtGNBO9iIhIOVeWIewRa+08Y4wXmGqM2QFkAvmrcW8HqhljvNba\nWBm2q9xau9YTD1xub9fatYWhq2ZNd46ujh2jdOoU4dhjFbpERESSSZmFMGvtvPjPmDFmGnAGsBGo\nihvAMoDfDiaAZWZWLc2mJsz69fDll4WXFSsK76tRA/r0gTPOcC8nnODF4/ECgYS1V0RERIqvTEKY\nMcYAHa21L8c3NQPGAh8DHYB3gY7x2we0adOOA++UBDZu9DBzpq+gt2v58sKerowMh/POi9CxY5SO\nHd2zF72Fd7N5cwIaLCIiIodkfx1HZdUTth34gzGmLnAksNpa+z9jzATgkXhIawIMLaP2JMSWLTBz\nZmEh/dKlhXNCVKnicNZZkYJC+pNO0pQRIiIiFZnHcZLurDknWXrCtm2DWbN8BcX0S5Z4cRy3cCs9\n3aFNmyidO7uF9K1aad1FERGRiiYzs+o+K7b1sV+CcnJgxgxfwbQRixd7icXc331KilMwtNixY5TW\nraMEgwlusIiIiCSMQlgJuvHGNCZOdH+lgYBD27bR+NmLUU49NUpqaoIbKCIiIuWGQlgJuvrqECee\nGKVDhyht2mjtRREREdk31YSJiIiIlJL91YR593WHiIiIiJQehTARERGRBFAIExEREUkAhTARERGR\nBFAIExEREUmAhE9RYYw5E+gDbACw1v4jsS0SERERKX0J7QkzxqQBzwG3xsNXS2PMGYlsk4iIiEhZ\nSPRwZAdglbU2Er89A+iZwPaIiIiIlIlEh7BaQNGZV7fHt4mIiIhUaImuCdsIZBS5nRHftj+ezMyq\npdciERERkTKQ6J6wWUADY0wgfrsj8HEC2yMiIiJSJhK+dmT87MhLcXvAwtbaBxLaIBEREZEykPAQ\nJiIiIlIZJXo4UkRERKRSUggTERERSQCFMBEREZEEUAgTERERSQCFMBEREZEEUAgTERERSQCFMBER\nEZEEUAgTERERSQCFMBEREZEEUAgTERERSQCFMBEREZEEUAgTERERSQCFMBEREZEEUAgTERERSQCF\nMBEREZEEUAgTERERSQCFMBEREZEEUAgTERERSQD/oR5gjPEAHwGzgRTgWOA6IB14BFgBNAWGWWs3\nxY8ZCmQA1YDPrbUfxbe3AgYCK4FawBBrbewwX5OIiIhIuVfcnrCZ1toHrbX34oavi4GHcAPWo8A4\n4EkAY0xboJu19j7gr8CTxpiq8cd5HbjbWvswEAX6Fv+liIiIiCSPQw5h1lrHWvsQgDHGD9QDlgI9\ngVnx3WYAf4hf75W/3VobBbKArsaYJkBqfm9Z/JiexXwdIiIiIkml2DVhxphzcIclx1trF+AOJ+6I\n370dqG6M8e6xPf++WvvZLiIiIlLhFTuEWWs/s9b2AJoYYwYAG4D8YcYM4Ld4fdfGItvz79sYv2T8\nznYRERGRCu+QQ5gxprkx5g9FNq0EGgMfAx3i2zrFb1N0uzEmABwPTLXWrgCyjTH5vV8dixyzT2lp\njuPx4PTti7NtGw7ooosuuuiiiy66lNvLPnkcZ7/37yVey/UYsAAI4oaqQUAY9+zI1UAT4M4iZ0cO\nAWrgnh05wVo7Pr69ZfzYVfH7hx7o7MilS3GuuCLKt9/6OOaYGM8+m8vpp0cP6TWIiIiIlIXMzKqe\nfd13yCGsHHDWr9/BU08FGT48SCwGAwaEueuuPFJSEt00ERERkUIVLoRt2uTW88+b52XgwDRWrvTS\nvHmUkSNzOfFETTMmIiIi5cP+QlhSz5h/2mkxvvhiF337hsjK8nHuuek8+2yAqEYnRUREpJxL6p6w\noiZN8jF4cCobN3pp3z7Cs8/m0qBB0r02ERERqUAqbE9YUWedFWXKlGx69gwze7afbt2q8OabfpIv\nY4qIiEhlUGFCGMBRRzm8/HIuzzyTA8CgQWlcd10qmzfvM4SKiIiIJESFCmEAHg9cfnmEKVN2cfrp\nET75JEDXrul89pkv0U0TERERKVDhQli++vUdxo7N4f77c9m2zcPVV6czZEgKO3cmumUiIiIiFagw\nf3+WLPFy882pLFnio1GjGCNG5NCmjaayEBERqQyysr5n5Mh/E4mEadu2A47jEAqFCIdD3HLLbXvt\n//bbb3DZZX8suP3BB++xfPkyqlevzvr166hZM5ObbvrLQT33/grz/cV4LUnnhBNiTJyYzaOPBhkx\nIsj556dz660hhgwJEQwmunUiIiKVw/33p/DRRyUbPc4/P8L99+ftd5/mzU/klFNOJTc3h+uu6w9A\nKBRi/vw5v7v/O+8UhrDs7F289NJzjB//OQCxWIynnnqsRNp+yL+J+LJFDwLzgfrAr9baB4wxJwO3\nAkuAE4F7rLVrjTENgU+Bn+MPMd9ae3v8sVoBA3HXn6wFDDnQskXFlZIC990X4uyzo9xySyr/+lcK\nkyf7GTkyl+OOU6+YiIhIZRGJRHjuuWcYNGjIXvd98cUkduzYwZgxL9KgQSO6dOkGwFtv/ZfzzuvJ\nkUdWY+jQO0ukHcVZO/I0oK619qP47e+Ba4DRQF9r7SJjTC/gBmtt73gI62qt/c/vPNZioLu1dpMx\n5gnge2vtmAM04ZCHI/e0Ywfcc08qb7wRIDXV4d578+jXL4y3wlbIiYiIVG4vv/wCM2dOp1Wrk4nF\nHLxez+8ORQJceumFvPPOuILbq1at5LXXxvD117No2LAR1157Pe3adTio5y3R4Uhr7bw9NnmAnUBT\nYE182wrgjCL7XGCMycRdwPt/1tqseI9aav4i38AM4CrgQCHssFWtCk8/ncs550QYOjSFu+9OZeJE\nP//+dy5HH510NXIiIiJyEFq3PpWbb74VgLVr1+xnz92zQKNGjbn33n/gOA5ffTWZu+++nbFjPyEj\nI+Ow2nNYfT/GmN7ARGvtD8B0oH38rrZAFWOMF9gE3GutfRJ4BvjYGJOBO/xYtEtre3xbmenZM8JX\nX2VzzjkRpk7107VrFcaOrRRlciIiIpXaMcfU3+d9Pp87rdWyZT/yyy8/88gjDwDg8Xjo3LkbKSkp\nJdKGYicOY0w3oJu1dnB80zXAIGNMU9xwtT5e35UNZAFYazcaYzYArYB1QNEImQFsLG57iqt2bYfX\nXsvh9dcD3HtvCjfdlMbEiWEefTSXatXKujUiIiJS0pYuzeLbbxcSiUT46qvJdOt25n7379ChEyNG\nPA3Atddez/bt23nmmX9RpUoVfv55PQMG3HLYvWBQzCkqjDE9gU7W2ruMMXWBhkC2tXZR/P6zcQPa\n3caYa4CF1trvjDEBYDnQ0Vq7xhizCDgrHs4OuibskBt8kJYtg2uvhVmzoF49GDMGzj67tJ5NRERE\nKoF91oQVpzC/NTAFmBt/4HRgBO6Zko1xe70ygIestXnGmDOAG4FvcOvGpltrX40/VktgELAKqAEM\nPYizIw+7MH9/IhF49tkgjz0WJBLxcMMNIe65J4/09FJ7ShEREamg9leYXykmay2ORYvcCV5/+MFH\ns2ZRRo7MpVUrTWUhIiJSUUyYMH6vbT169CrR51AIK6acHPjnP1N44YUgfr/D0KEhBg0K4VftvoiI\niBwEhbDDNGWKj1tvTWX9ei+nnhplxIgcmjRJut+biIiIlLH9hTBNT3oQunaN8tVXu+jTJ8z8+T66\nd6/Cq68GSL78KiIiIuWFQthBqlYNnnsul+efzyEQgNtvT+Wqq9LYsGGfAVdERERknxTCDtFFF0WY\nOnUXXbtGmDTJT9eu6YwfryIxEREROTQKYcVQt67DW2/l8PDDuWRne7j++jRuuSWV7dsT3TIRERFJ\nFgphxeT1Qr9+YSZPzqZVqyhvvRXgjDOqMGmSj2g00a0TERGR8k5nR5aAcBieeirI8OFBolEPderE\n6N07wsUXh2nZMoZHZWMiIiKVkqaoKCOLFnl59dUAH34YYNs293d+7LEx+vQJ06dPmGOPTbrfvmGU\ncAAAIABJREFUtYhIUtuwwcMTTwRp0iTG1VeHqVo10S2SyqZEQ5gxpgnwIDAfd6miX621DxhjTgZu\nBZYAJwL3WmvXxI8ZiruUUTXgc2vtR/HtrYCBwEqgFjAk0csWlYS8PPjySx9jxwaYONFPTo77+z/5\n5Ch9+oTp3TtCnToKZCIipemzz3wMHpzK5s1u5U3Vqg7XXBOmf/8Q9erpb7CUjZIOYacBdYsEqe+B\na4DRQF9r7SJjTC/gBmttb2NMW+A+a20vY4wPd23JU621O4wxi4Hu1tpNh7KAd3kPYUXt3AkTJvgZ\nOzbAV1/5iEY9eDwOnTpFueiiCL16halWLdGtFBGpOLKz4e9/T2HMmCDBoMOwYXnk5Xl46aUAmzZ5\n8fsdeveOMGBAiBYttBydlK5SHY40xiwBeuP2jDWw1v5mjDkBmGWtPdIY8w8gz1r7z/j+44AXcXvM\nJlprm8W3XwRcZa295ABPmVQhrKjNmz18+KGfsWP9zJnjTmsRCDiceWaEiy+OcPbZES0ULiJyGBYv\n9jJggLvu7/HHRxk1KpcTT3SDVm4ujB3rZ9SoINb6AOjcOcLAgSHOOCOq+l0pFaU2Y74xpjdukPoB\nmA60j9/VDqhijPHiDjMWTU3b49v2tb3CqlnT4frrw4wfn8O8eTu55548mjaN8emnAfr3T+PEE49g\n4MBUvvjCRySS6NaKiCSPWAxGjQrQo0c6P/zgo3//EBMnZhcEMIDUVLjyyghTpmTzxhvZdO4cYdo0\nP1dckU7Xrum88YafvLwEvgipdIrdE2aM6Qb0ttYOjt+uCQwCNuGGq39YaxscoCfsM2tt0/j2g+4J\nK1aDy7HFi+GNN+B//4OffnK3ZWbCZZfBlVdChw7oG5qIyD6sXw99+8KkSVC7NowZAz16HNyxCxbA\nk0/CW29BNAp16sCgQfDnP0ONGqXbbqk0SnY40hjTE+hkrb3LGFMXaAhkW2sXxe8/G+hmrb3bGNMO\nt0i/lzEmAHwHtLHWbjfGLALOstZurKg1YYfCcWDuXC9jxwb48EN/QTFpgwYxLrooTJ8+EZo3V/2C\niEi+jz/2c9ttqfz2m4ezz44wfHgumZmH/rm2dq2HF18M8tprAXbu9JCe7nDllWFuvDFEo0YV7ru/\nlKGSLsxvDUwB5uKmu3RgBO6Zko1xC+8zgIestXnxY4YANXDPjpxgrR0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UwkTKmWXLPDz0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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d050ed0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data[['SX5E', 'sigma**2', 'F_tS']].plot(subplots=True,\n",
    "                                       color='blue',\n",
    "                                       figsize=(10, 9));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h5 = pd.HDFStore('data/var_data.h5', 'a')\n",
    "h5['var_future'] = data\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Comparison of Variance Swap and Future"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "h5 = pd.HDFStore('data/var_data.h5', 'r')\n",
    "var_swap = h5['var_swap']\n",
    "h5.close()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>F_tS</th>\n",
       "      <th>V_t</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2015-06-01</th>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-02</th>\n",
       "      <td>-83</td>\n",
       "      <td>-82.332277</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-03</th>\n",
       "      <td>-125</td>\n",
       "      <td>-124.049833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-04</th>\n",
       "      <td>-140</td>\n",
       "      <td>-139.593571</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-05</th>\n",
       "      <td>-108</td>\n",
       "      <td>-107.644092</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-08</th>\n",
       "      <td>-137</td>\n",
       "      <td>-136.380856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-09</th>\n",
       "      <td>-201</td>\n",
       "      <td>-200.634978</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-10</th>\n",
       "      <td>-198</td>\n",
       "      <td>-196.905901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-11</th>\n",
       "      <td>-224</td>\n",
       "      <td>-223.189008</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-12</th>\n",
       "      <td>-173</td>\n",
       "      <td>-172.129074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-15</th>\n",
       "      <td>-88</td>\n",
       "      <td>-86.887791</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-16</th>\n",
       "      <td>-142</td>\n",
       "      <td>-141.190592</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-17</th>\n",
       "      <td>-236</td>\n",
       "      <td>-234.822528</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-18</th>\n",
       "      <td>-311</td>\n",
       "      <td>-309.629583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015-06-19</th>\n",
       "      <td>-393</td>\n",
       "      <td>-391.004773</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            F_tS         V_t\n",
       "Date                        \n",
       "2015-06-01     0    0.000000\n",
       "2015-06-02   -83  -82.332277\n",
       "2015-06-03  -125 -124.049833\n",
       "2015-06-04  -140 -139.593571\n",
       "2015-06-05  -108 -107.644092\n",
       "2015-06-08  -137 -136.380856\n",
       "2015-06-09  -201 -200.634978\n",
       "2015-06-10  -198 -196.905901\n",
       "2015-06-11  -224 -223.189008\n",
       "2015-06-12  -173 -172.129074\n",
       "2015-06-15   -88  -86.887791\n",
       "2015-06-16  -142 -141.190592\n",
       "2015-06-17  -236 -234.822528\n",
       "2015-06-18  -311 -309.629583\n",
       "2015-06-19  -393 -391.004773"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "comp = pd.DataFrame({'F_tS': data['F_tS'] - 3000,\n",
    "                     'V_t': var_swap['V_t']}, index=data.index)\n",
    "comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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jrbXzdnG3jcAoa+1qY0wj4F1jzLHJxym9xlO0w2OL1DouF9x/f4CzznLx8MM5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\nSZI0EUxaJEnSRDBpkSRJE+E/MXb8GQ0nhLAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d223fd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "comp.plot(style=['b', 'ro'], figsize=(9, 5));"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11d5b9050>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "(comp['F_tS'] - comp['V_t']).plot(style='r^', figsize=(9, 5));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"http://hilpisch.com/tpq_logo.png\" alt=\"The Python Quants\" width=\"35%\" align=\"right\" border=\"0\"><br>\n",
    "\n",
    "<a href=\"http://tpq.io\" target=\"_blank\">http://tpq.io</a> | <a href=\"http://twitter.com/dyjh\" target=\"_blank\">@dyjh</a> | <a href=\"mailto:team@tpq.io\">team@tpq.io</a>\n",
    "\n",
    "**DX Analytics** |\n",
    "<a href=\"http://dx-analytics.com\">http://dx-analytics.com</a>\n",
    "\n",
    "**Quant Platform** |\n",
    "<a href=\"http://quant-platform.com\">http://quant-platform.com</a>\n",
    "\n",
    "**Python for Finance Books** |\n",
    "<a href=\"http://books.tpq.io\" target=\"_blank\">http://books.tpq.io</a>\n",
    "\n",
    "**Python for Finance Training** |\n",
    "<a href=\"http://training.tpq.io\" target=\"_blank\">http://training.tpq.io</a>"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
